title: A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text.
abstract: BACKGROUND
  To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge.
  
  
  METHODS
  We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches.
  
  
  RESULTS
  Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText.
  
  
  BACKGROUND
  An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: The National Centre for Text Mining and School of Computer Science and School of Chemistry and the Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK. makoto.miwa@manchester.ac.uk
authors.forename: Makoto
authors.initials: M
authors.lastname: Miwa
authors.forename: Tomoko
authors.initials: T
authors.lastname: Ohta
authors.forename: Rafal
authors.initials: R
authors.lastname: Rak
authors.forename: Andrew
authors.initials: A
authors.lastname: Rowley
authors.forename: Douglas B
authors.initials: DB
authors.lastname: Kell
authors.forename: Sampo
authors.initials: S
authors.lastname: Pyysalo
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001669:Biochemical Phenomena; D057225:Data Mining / Q000379:methods*; D016239:MEDLINE; D060388:Support Vector Machine